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Next-Generation Space Navigation: Opportunistic Signals, Autonomous Orbit Determination, and Security...
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Next-Generation Space Navigation: Opportunistic Signals, Autonomous Orbit Determination, and Security Enhancement

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Microwave and Wireless Communications".

Deadline for manuscript submissions: 15 October 2026 | Viewed by 1938

Special Issue Editors

Dr. Jiajia Chen

E-Mail Website
Guest Editor
Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
Interests: navigation positioning; spoofing detection; signal and information processing
Special Issues, Collections and Topics in MDPI journals
Dr. Ming Gao

E-Mail Website
Guest Editor
Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
Interests: remote sensing; geodesy and surveying
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Ying Xu

E-Mail Website
Guest Editor
Aerospace Information Research Institute, Chinese Academy of Sciences, No. 9 Dengzhuang South Road, Haidian District, Beijing 100094, China
Interests: navigation positioning; signal enhancement technology
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Global Navigation Satellite Systems (GNSS) underpin modern navigation in near-Earth space. However, their inherent limitations—including weak signals and constrained geometry—pose significant challenges to accuracy, availability, and security for deep space exploration, large-scale low Earth orbit (LEO) constellations, and operations in contested environments. Addressing these challenges necessitates a paradigm shift in navigation technology.

This Special Issue, "Next-Generation Space Navigation: Opportunistic Signals, Autonomous Orbit Determination, and Security Enhancement," aims to showcase the latest advances in building a more robust, autonomous, and secure next-generation space navigation architecture. It focuses on leveraging diverse information sources and intelligent algorithms to complement or augment traditional GNSS.

We invite original research and review articles exploring key topics including, but not limited to, the following:

  • Navigation using LEO opportunistic signals (e.g., from communication constellations like Starlink).
  • Deep space autonomous navigation using pulsars, optical images, and other celestial sources.
  • Advanced methods for satellite precise orbit determination.
  • Multi-sensor integrated navigation algorithms fusing heterogeneous data.
  • Spoofing detection and mitigation techniques to ensure navigation resilience.

This special issue will provide a platform for researchers and engineers to share insights that push space navigation toward a more intelligent and resilient future, critical for upcoming deep space missions and complex orbital operations.

We look forward to your valuable contributions!

Dr. Jiajia Chen
Dr. Ming Gao
Dr. Ying Xu
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Electronics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • opportunistic signals
  • autonomous navigation
  • precise orbit determination
  • spoofing detection

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Published Papers (4 papers)

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24 pages, 3090 KB  
Article
A Convolutional Neural Network Framework for Opportunistic GNSS-R Wind Speed Retrieval over Inland Lakes
by Yanan Ni, Jiajia Chen, Jiajia Jia and Xinnian Guo
Electronics 2026, 15(7), 1501; https://doi.org/10.3390/electronics15071501 - 3 Apr 2026
Viewed by 337
Abstract
Global Navigation Satellite System Reflectometry (GNSS-R) provides a promising approach for wind speed retrieval over inland waters, with relevance to wind energy assessment and lake–atmosphere exchange studies. Existing GNSS-R wind retrieval methods are well established for open oceans but face major challenges over [...] Read more.
Global Navigation Satellite System Reflectometry (GNSS-R) provides a promising approach for wind speed retrieval over inland waters, with relevance to wind energy assessment and lake–atmosphere exchange studies. Existing GNSS-R wind retrieval methods are well established for open oceans but face major challenges over inland waters, where coherent scattering dominates and traditional ocean models produce large systematic biases. Unlike open oceans, inland waters are dominated by coherent scattering due to limited fetch, resulting in Delay-Doppler Maps (DDM) with highly concentrated energy and minimal spreading. These characteristics render conventional ocean-based retrieval models—built on incoherent scattering assumptions—often inadequate. To overcome this, we develop a lightweight convolutional neural network (CNN) tailored to the coherent regime, using raw CYGNSS DDM as input for end-to-end wind speed regression. Cross-seasonal validation over Lake Victoria and Lake Hongze shows that the model robustly captures wind-driven spatiotemporal patterns aligned with ERA5. Notably, ERA5 reanalysis winds exhibit uncertainties over inland waters, with a root mean square error (RMSE) of 1.5–2.5 m/s against in situ buoys. The model yields a low RMSE (<0.7 m/s) in reconstructing ERA5-resolved wind patterns. This work extends GNSS-R to inland waters, offering a lightweight, deployable remote sensing solution for wind energy and lake–atmosphere research. Full article
(This article belongs to the Special Issue Next-Generation Space Navigation: Opportunistic Signals, Autonomous Orbit Determination, and Security Enhancement)
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16 pages, 1864 KB  
Article
Research on Inertial Navigation-Aided GNSS Integrity Monitoring Algorithm Under Constraints
by Jie Zhang, Zhibo Fang and Jiashuang Yan
Electronics 2026, 15(6), 1333; https://doi.org/10.3390/electronics15061333 - 23 Mar 2026
Viewed by 551
Abstract
To address the challenge that prolonged interruptions of Global Navigation Satellite System (GNSS) signals—such as those caused by urban obstructions—hinder signal re-locking and thereby reduce the number of available satellites for integrity monitoring algorithms, this study proposes an inertial navigation-assisted GNSS re-locking method [...] Read more.
To address the challenge that prolonged interruptions of Global Navigation Satellite System (GNSS) signals—such as those caused by urban obstructions—hinder signal re-locking and thereby reduce the number of available satellites for integrity monitoring algorithms, this study proposes an inertial navigation-assisted GNSS re-locking method based on vehicle motion information constraints. This method leverages vehicle motion constraints to confine the primary direction of Inertial Navigation System (INS) velocity errors to the vehicle’s forward direction. Upon GNSS signal recovery, frequency error compensation is employed to mitigate Doppler errors of the previously obstructed satellites. Simulation results show that this method significantly improves the re-lock capability after a long period of satellite signal interruption, increasing the number of available satellites from 7 to 10 and optimizing the satellite geometry. At a horizontal alarm threshold of 80 m, the availability of the GNSS integrity monitoring algorithm reaches 95.7%, which is 53.7 percentage points higher than the unassisted scheme. Moreover, it can achieve 100% fault detection and identification rate even with a pseudorange deviation of 82 m, significantly improving the performance of the integrity monitoring algorithm. Full article
(This article belongs to the Special Issue Next-Generation Space Navigation: Opportunistic Signals, Autonomous Orbit Determination, and Security Enhancement)
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20 pages, 1965 KB  
Article
APF-Driven Lightweight UAV Swarm Trajectory Optimization in GNSS-Denied Air–Terrestrial Navigation
by Ruocheng Guo, Hong Yuan, Xiao Chen and Wen Li
Electronics 2026, 15(6), 1207; https://doi.org/10.3390/electronics15061207 - 13 Mar 2026
Viewed by 323
Abstract
To enable autonomous route planning for UAV swarms in dynamic air–terrestrial cooperative navigation scenarios within GNSS-denied environments, this paper proposes a lightweight framework based on the Artificial Potential Field (APF) method. In the considered architecture, UAVs act as mobile transit navigation nodes that [...] Read more.
To enable autonomous route planning for UAV swarms in dynamic air–terrestrial cooperative navigation scenarios within GNSS-denied environments, this paper proposes a lightweight framework based on the Artificial Potential Field (APF) method. In the considered architecture, UAVs act as mobile transit navigation nodes that relay positioning information from sparse ground anchors to terrestrial users. For TOA-based cooperative positioning, the instantaneous geometric configuration of the UAV swarm significantly affects the overall system accuracy. Therefore, the impact of UAV positions on the end-to-end navigation performance is rigorously analyzed, yielding a comprehensive Dilution of Precision (DOP) matrix for the entire air–terrestrial system. By applying the Schur complement, the global performance metric is decomposed, resulting in a scalar evaluation function that directly reflects the geometric quality of the configuration. In practical scenarios involving dynamic and heterogeneous users, real-time trajectory adaptation of the UAV swarm is essential to continuously optimize user positioning accuracy. To this end, an APF-based autonomous joint route planning approach is developed. The potential field is constructed directly from the derived geometric evaluation model, where its negative gradient generates virtual forces that autonomously guide the UAV swarm. This elegantly bridges high-level navigation performance optimization with low-level motion control of the swarm. The simulation results show a 76.1% improvement in the average comprehensive GDOP for users compared to the baseline of hovering UAVs, validating the effectiveness and real-time capability of the proposed lightweight framework. Full article
(This article belongs to the Special Issue Next-Generation Space Navigation: Opportunistic Signals, Autonomous Orbit Determination, and Security Enhancement)
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20 pages, 6854 KB  
Article
TARTS: Training-Free Adaptive Reference-Guided Traversability Segmentation with Automated Footprint Supervision and Experimental Verification
by Shuhong Shi and Lingchuan Zeng
Electronics 2026, 15(6), 1194; https://doi.org/10.3390/electronics15061194 - 13 Mar 2026
Viewed by 336
Abstract
Autonomous mobile robots require robust traversability perception to navigate safely in diverse outdoor environments. However, traditional deep learning approaches are data-hungry, requiring large-scale manual annotations, and struggle to adapt quickly to unseen environments. This paper introduces TARTS (Training-free Adaptive Reference-guided Traversability Segmentation), a [...] Read more.
Autonomous mobile robots require robust traversability perception to navigate safely in diverse outdoor environments. However, traditional deep learning approaches are data-hungry, requiring large-scale manual annotations, and struggle to adapt quickly to unseen environments. This paper introduces TARTS (Training-free Adaptive Reference-guided Traversability Segmentation), a novel framework combining one-shot prototype initialization with trajectory-guided online adaptation for terrain segmentation. Using a single reference image of desired traversable terrain, TARTS establishes an initial prototype from pre-trained DINO Vision Transformer (ViT) features. The system performs segmentation through superpixel-based feature aggregation and valley-emphasis Otsu thresholding while continuously refining the prototype via Exponential Moving Average (EMA) updates driven by automated footprint supervision from the robot’s traversed trajectory. Extensive experiments on our introduced Reference-guided Traversability Segmentation Dataset (RTSD) and the challenging Off-Road Freespace Detection (ORFD) benchmark demonstrate strong performance, achieving 94.5% IoU on RTSD and 94.1% IoU on ORFD, outperforming state-of-the-art supervised methods that require multi-modal inputs and dedicated training. The framework maintains efficient performance (17–24 FPS) on embedded platforms, enabling practical deployment with only a reference image as initialization. Full article
(This article belongs to the Special Issue Next-Generation Space Navigation: Opportunistic Signals, Autonomous Orbit Determination, and Security Enhancement)
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